State and Output Feedback Nonlinear Model Predictive Control: An Overview

Findeisen, Rolf; Imsland, Lars; Allgöwer, Frank; Foss, Bjarne A.

doi:10.3166/ejc.9.190-206

Cited by 301 publications

(199 citation statements)

References 81 publications

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“…The fluorescence intensity of a single cell arbitrarily chosen at the start of the experiment, or the average fluorescence intensity of the cell population, was sent to a state estimator (extended Kalman filter discussed in SI Materials and Methods) connected to a model predictive controller (31). Model Predictive Control (MPC) is an efficient framework well-adapted Yeast cells grew as a monolayer in a microfluidic device that was used to rapidly change the cells' osmotic environment (blue frame) and image their response.…”

mentioning

confidence: 99%

Long-term model predictive control of gene expression at the population and single-cell levels

Uhlendorf

Miermont

Delaveau

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

220

237

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Gene expression plays a central role in the orchestration of cellular processes. The use of inducible promoters to change the expression level of a gene from its physiological level has significantly contributed to the understanding of the functioning of regulatory networks. However, from a quantitative point of view, their use is limited to short-term, population-scale studies to average out cell-to-cell variability and gene expression noise and limit the nonpredictable effects of internal feedback loops that may antagonize the inducer action. Here, we show that, by implementing an external feedback loop, one can tightly control the expression of a gene over many cell generations with quantitative accuracy. To reach this goal, we developed a platform for real-time, closed-loop control of gene expression in yeast that integrates microscopy for monitoring gene expression at the cell level, microfluidics to manipulate the cells' environment, and original software for automated imaging, quantification, and model predictive control. By using an endogenous osmostress responsive promoter and playing with the osmolarity of the cells environment, we show that long-term control can, indeed, be achieved for both time-constant and time-varying target profiles at the population and even the single-cell levels. Importantly, we provide evidence that real-time control can dynamically limit the effects of gene expression stochasticity. We anticipate that our method will be useful to quantitatively probe the dynamic properties of cellular processes and drive complex, synthetically engineered networks.model based control | computational biology | high osmolarity glycerol pathway | quantitative systems biology U nderstanding the information processing abilities of biological systems is a central problem for systems and synthetic biology (1-6). The properties of a living system are often inferred from the observation of its response to static perturbations. Timevarying perturbations have the potential to be much more informative regarding the dynamics of cellular functions (7-12). Currently, it is not possible to precisely perturb protein levels in an analogous manner, even though this perturbation would be instrumental in our understanding of gene regulatory networks. Indeed, despite the development of novel regulatory systems, including various RNA-based solutions (13), transcriptional control by means of inducible promoters is still the preferred method for manipulating protein levels (14, 15). Unfortunately, inducible promoters have several generic limitations. First, there is a significant delay between gene expression activation and effective protein synthesis. Second, many cellular processes can interfere with gene expression through internal feedback loops whose effects are hard to predict. Third, the process of gene expression shows significant levels of noise (16)(17)(18). Given these limitations, novel experimental strategies are required to gain quantitative, real-time control of gene expression in vivo.Here, we see the...

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mentioning

confidence: 99%

Long-term model predictive control of gene expression at the population and single-cell levels

Uhlendorf

Miermont

Delaveau

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

220

237

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“…Similar sampled-data frameworks using continuous-time models and sampling the state of the plant at discrete instants of time were adopted in [4,7,8,6,16] and are becoming the accepted framework for continuous-time MPC. It can be shown that with this framework it is possible to address -and guarantee stability, and robustness of the resultant closed-loop system -a very large class of systems, which are possibly nonlinear, time-varying, and nonholonomic.…”

Section: Compute the Open-loop Optimal Controlū : [Tmentioning

confidence: 99%

Model Predictive Control of Vehicle Formations

Fontes

Caldeira

2009

Optimization and Cooperative Control Strategies

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Abstract. We propose a two-layer scheme to control a set of vehicles moving in a formation.The first layer, the trajectory controller, is a nonlinear controller since most vehicles are nonholonomic systems and require a nonlinear, even discontinuous, feedback to stabilize them. The trajectory controller, a model predictive controller, computes centrally a bang-bang control law and only a small set of parameters need to be transmitted to each vehicle at each iteration.The second layer, the formation controller, aims to compensate for small changes around a nominal trajectory maintaining the relative positions between vehicles. We argue that the formation control can be, in most cases, adequately carried out by a linear model predictive controller accommodating input and state constraints. This has the advantage that the control laws for each vehicle are simple piecewise affine feedback laws that can be pre-computed off-line and implemented in a distributed way in each vehicle.Although several optimization problems have to be solved, the control strategy proposed results in a simple and efficient implementation where no optimization problem needs to be solved in real-time at each vehicle.

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“…In recent years, MPC has been applied over a wide range, from chemicals to transportation industries. 20,21) MPC is a form of control in which the controller output is obtained by solving an open-loop optimal control problem on-line at each sampling instant using the current state of the plant as the initial state. Optimization yields an optimal control sequence and the first control in this sequence is applied to the plant.…”

Section: Model Predictive Control Strategymentioning

confidence: 99%

Model Predictive Controller Design to Suppress Rate-Limiter-Based Pilot-Induced Oscillations

Liang

Yamada

Sakamoto

et al. 2007

Trans. Japan Soc. Aero. S Sci.

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Most severe pilot-induced oscillations (PIOs) are characterized as the presence of actuator rate limiting. Rate limiting causes phase lag and magnitude reduction between the limiter input and output when the input signal rate exceeds the rate limitation. This is the main cause of PIOs. Herein, two controllers, in which actuator rate limiting is redefined as input constraints, are designed based on the model predictive control (MPC) strategy and applied to the pilot-aircraft loop in both parallel and serial manners. In the parallel implementation, the MPC controller can suppress rate-limiter-based PIOs. In the serial implementation, the MPC controller can compensate the phase lag due to rate limiting, and hence, help the pilot prevent PIOs from occurring in advance.

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State and Output Feedback Nonlinear Model Predictive Control: An Overview

Cited by 301 publications

References 81 publications

Long-term model predictive control of gene expression at the population and single-cell levels

Long-term model predictive control of gene expression at the population and single-cell levels

Model Predictive Control of Vehicle Formations

Model Predictive Controller Design to Suppress Rate-Limiter-Based Pilot-Induced Oscillations

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